Searcharxiv⌕ Search

arXiv · cond-mat/9911433

Non-ohmic hopping transport in a-YSi: from isotropic to directed percolation

Abstract

Variable range hopping transport has been investigated in amorphous YSi from 30 mK to 2 K as a function of the temperature T and electric field F. For F=0, where conduction is along sinuous paths (isotropic percolation), the conductance G depends on T according to an Efros-Shklovskii law. The nonlinear I-V's were studied up to very high fields for which G no longer depends on T (directed percolation : current paths along F). We show that heating effects are negligible. Then, we show that for low F values, ln(G(F,T))=ln(G(0,T))-eFL/kT. The order of magnitude (5-10 nm) and the T dependence of L agree with theoretical predictions. From L, we extract the dielectric constant. The very high electric field data do not agree with the theoretical predictions, which could be due to tunneling across the mobility edge. For intermediate electric fields our data evidence the influence of the straightening of the paths (transition from isotropic to directed percolation). The statistical properties of the segments of the paths where the current flows against the electrical force are extracted for the first time.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

F. Ladieu, D. L'Hôte, R. Tourbot. 1999-11-26. Non-ohmic hopping transport in a-YSi: from isotropic to directed percolation. https://doi.org/10.1103/physrevb.61.8108

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Tunable periodic cascade of delocalization transitions in non-Hermitian IAAF model

Cascade of delocalization transitions provides a route to multi-step control of delocalization, yet achieving a programmable periodic cascade remains a challenge. Here, we demonstrate a tunable periodic cascade of delocalization transitions in a non-Hermitian interpolating Aubry-André-Fibonacci (IAAF) model by introducing an imaginary phase shift into the quasiperiodic potential. Such a periodic cascade of delocalization transitions arises from the competition between kinetic-energy-facilitated delocalization and gain-loss-regulated localization. In numerical static localization characterizations, successive regular stripe patterns for localized and extended states emerge in phase diagrams. We also derive an analytical expression for the cascade period and find that it relies solely on the imaginary phase shift. This provides a direct way of continuously tuning the cascade period via a single control parameter. Complementary static and dynamical diagnostics consistently confirm this periodic cascade of delocalization transitions in the non-Hermitian IAAF system. Our findings identify a novel class of delocalization transitions with tunable cascading periods, opening a route toward experimentally programmable control of delocalization.

cond-mat.dis-nn↗

Microscopic Mechanism and Control of Reentrant Localization

Increasing quasiperiodic modulation can restore extended states after localization, raising the question of how the same modulation drives both localization and recovery. We identify a microscopic mechanism in quasiperiodic dimer chains by resolving scattering between Fourier channels. Specifically, scattering into neighboring channels and back shifts the spectral edge relative to a localization boundary determined by a neutral auxiliary resonance. Their successive crossings drive the same spectral packet with fixed integrated-density-of-states (IDS) labels into localization and back into an extended regime. This recovery persists across finite parameter ranges in both gap-opening families, including relative-phase detuning. Furthermore, an exact sublattice mapping enables compensated control of the recovery window and predicts recovery thresholds for individual IDS labels.

cond-mat.dis-nn↗

Dynamical mean-field theory for associative memory with block-correlated patterns

In associative memories storing correlated patterns, retrieval of individual memories can compete with symmetric mixed states. A dynamical description is needed to determine how the initial state selects between these outcomes. We extend the generating-functional dynamical mean-field theory (DMFT) for independent patterns to independent blocks of correlated binary patterns of fixed finite size. The resulting effective single-neuron process has a self-consistent cross-talk noise covariance and self-feedback coefficients that depend on the block-covariance eigenvalues. For hierarchically correlated patterns with nonmonotonic transfer, increasing the loading can permit retrieval from an imperfect initial state before further loading prevents it. DMFT reproduces the retrieval trajectories and basin geometry found in direct simulations. Near the lower boundary of this re-entrant region, direct networks reveal stationary saddles separating coexisting mixed and target-retrieval states. Parameter sensitivities of DMFT trajectories predict nearby shifts of this boundary. At the tested upper boundary, retrieval remains accessible from initial states with larger target overlap, indicating a change in basin access. Under fixed-point convergence assumptions, the theory recovers the equations of generalized self-consistent signal-to-noise analysis.

cond-mat.dis-nn↗